Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+# Load model + tokenizer
+model_name = "nikhil061307/contrastive-learning-bert-added-token-v5"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function for inference: encode text -> embedding
+def encode(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        # mean pooling over sequence length
+        embedding = outputs.last_hidden_state.mean(dim=1)
+    return embedding
+
+# Compare two sentences
+def compute_similarity(sentence1, sentence2):
+    emb1 = encode(sentence1)
+    emb2 = encode(sentence2)
+    similarity = F.cosine_similarity(emb1, emb2).item()
+    return {"similarity": round(similarity, 4)}
+
+# Gradio UI
+demo = gr.Interface(
+    fn=compute_similarity,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter first sentence..."),
+        gr.Textbox(lines=2, placeholder="Enter second sentence...")
+    ],
+    outputs="json",
+    title="Contrastive Learning BERT Similarity",
+    description="Enter two sentences to compute their semantic similarity using the fine-tuned BERT model."
+)
+
+if __name__ == "__main__":
+    demo.launch()